Speaker verification aims to determine whether a pair of audios corresponds to the same speaker. Given speech signals, speaker verification systems are able to extract speaker identity patterns from the characteristics of the voice. These patterns can be both statistically modelled or encoded into discriminative speaker representations. Over the last few years, researchers have put huge effort on encoding these speaker characteristics into more discriminative speaker vectors. Current state-of-the-art speaker verification systems are based on dl approaches. These architectures are commonly trained as speaker classifiers in order to be used as speaker embedding extractors. Speaker embeddings are fixed-length vectors extracted from some of the last layers of these dnn [1]}. The most known representation is the x-vector [2]}, which has become state-of-the-art for speaker recognition and has also been used for other tasks such as language and emotion recognition [3]}, [4]}.
